Filtragem de percepções em agentes baseada em objetivos e no modelo de revisão de crenças data-oriented belief revision (DBR)
In scenarios where intelligent agents act and perceive data of environments with much information, identify only perceptions relevant to goals can be crucial for the agent reasoning cycle to be performed in time. As a solution to this problem, this work creates a model to filter perceptions based on...
| Autor principal: | Mendes, Guilherme Firmino |
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| Formato: | Dissertação |
| Idioma: | Português |
| Publicado em: |
Universidade Tecnológica Federal do Paraná
2016
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| Assuntos: | |
| Acesso em linha: |
http://repositorio.utfpr.edu.br/jspui/handle/1/1896 |
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| Resumo: |
In scenarios where intelligent agents act and perceive data of environments with much information, identify only perceptions relevant to goals can be crucial for the agent reasoning cycle to be performed in time. As a solution to this problem, this work creates a model to filter perceptions based on DBR (Data-oriented Belief Revision) model to be applied at BDI (Belief Desire Intention) agents. In order to do it, this work has extended and formalized some of the DBR model concepts making it applicable in computer programs. Among this work contributions are the extension and definition of the processes Focus (selection of perceived data) and Oblivion of inactive data; definition and formalization of perception Relevance models, calculations that allow to filter or discard data based on agent plans and their importance values;definition of Inactive Data storage models able to support different usage scenarios of BDI agents. The result was a generic and automated perception filter oriented to the goals of BDI agents. To opera- tionalize the model, it was implemented in the agent development plataform Jason. Empirical analysis have been done to assess the correctness and identify the impact on the processing time after the model application. The results indicate that the perception filtering model proposed in this work contributes to the computational performance of agents exposed to environments with much noise. |
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